Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.4 KiB
Average record size in memory128.3 B

Variable types

Text1
Numeric14
Categorical1

Alerts

AGE is highly overall correlated with CRIM and 7 other fieldsHigh correlation
CRIM is highly overall correlated with AGE and 8 other fieldsHigh correlation
DIS is highly overall correlated with AGE and 6 other fieldsHigh correlation
INDUS is highly overall correlated with AGE and 7 other fieldsHigh correlation
MEDV is highly overall correlated with AGE and 7 other fieldsHigh correlation
NOX is highly overall correlated with AGE and 8 other fieldsHigh correlation
PTRATIO is highly overall correlated with MEDV and 1 other fieldsHigh correlation
RAD is highly overall correlated with CRIM and 3 other fieldsHigh correlation
RM is highly overall correlated with MEDVHigh correlation
TAX is highly overall correlated with AGE and 7 other fieldsHigh correlation
TRACT is highly overall correlated with AGE and 8 other fieldsHigh correlation
ZN is highly overall correlated with AGE and 4 other fieldsHigh correlation
CHAS is highly imbalanced (63.7%)Imbalance
TRACT has unique valuesUnique
ZN has 372 (73.5%) zerosZeros

Reproduction

Analysis started2025-10-01 10:53:19.355525
Analysis finished2025-10-01 10:53:56.591767
Duration37.24 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

TOWN
Text

Distinct92
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:56.801314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.9743083
Min length4

Characters and Unicode

Total characters5047
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.4%

Sample

1st rowNahant
2nd rowSwampscott
3rd rowSwampscott
4th rowMarblehead
5th rowMarblehead
ValueCountFrequency (%)
boston157
22.0%
cambridge30
 
4.2%
hill26
 
3.6%
savin23
 
3.2%
roxbury23
 
3.2%
lynn22
 
3.1%
newton18
 
2.5%
somerville15
 
2.1%
south13
 
1.8%
east12
 
1.7%
Other values (87)375
52.5%
2025-10-01T10:53:57.137009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o618
 
12.2%
n465
 
9.2%
t389
 
7.7%
e378
 
7.5%
a270
 
5.3%
r264
 
5.2%
s254
 
5.0%
l250
 
5.0%
B220
 
4.4%
i219
 
4.3%
Other values (31)1720
34.1%

TRACT
Real number (ℝ)

High correlation  Unique 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2700.3557
Minimum1
Maximum5082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:57.266117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile430.5
Q11303.25
median3393.5
Q33739.75
95-th percentile4202.75
Maximum5082
Range5081
Interquartile range (IQR)2436.5

Descriptive statistics

Standard deviation1380.0368
Coefficient of variation (CV)0.51105742
Kurtosis-1.1960953
Mean2700.3557
Median Absolute Deviation (MAD)787
Skewness-0.43580814
Sum1366380
Variance1904501.7
MonotonicityNot monotonic
2025-10-01T10:53:57.414451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18051
 
0.2%
20111
 
0.2%
20211
 
0.2%
20221
 
0.2%
20311
 
0.2%
20321
 
0.2%
20331
 
0.2%
20411
 
0.2%
20421
 
0.2%
20431
 
0.2%
Other values (496)496
98.0%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
1011
0.2%
1021
0.2%
ValueCountFrequency (%)
50821
0.2%
50811
0.2%
50711
0.2%
50621
0.2%
50611
0.2%
50521
0.2%
50511
0.2%
50411
0.2%
50311
0.2%
50221
0.2%

LON
Real number (ℝ)

Distinct375
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-71.056389
Minimum-71.2895
Maximum-70.81
Zeros0
Zeros (%)0.0%
Negative506
Negative (%)100.0%
Memory size4.1 KiB
2025-10-01T10:53:57.548025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-71.2895
5-th percentile-71.202375
Q1-71.093225
median-71.0529
Q3-71.019625
95-th percentile-70.936
Maximum-70.81
Range0.4795
Interquartile range (IQR)0.0736

Descriptive statistics

Standard deviation0.075405348
Coefficient of variation (CV)-0.0010612043
Kurtosis1.1084808
Mean-71.056389
Median Absolute Deviation (MAD)0.0371
Skewness-0.20538473
Sum-35954.533
Variance0.0056859665
MonotonicityNot monotonic
2025-10-01T10:53:57.687636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.0695
 
1.0%
-71.044
 
0.8%
-71.034
 
0.8%
-71.0594
 
0.8%
-71.0554
 
0.8%
-71.024
 
0.8%
-71.04554
 
0.8%
-71.0754
 
0.8%
-71.0463
 
0.6%
-70.933
 
0.6%
Other values (365)467
92.3%
ValueCountFrequency (%)
-71.28951
0.2%
-71.28071
0.2%
-71.2691
0.2%
-71.26851
0.2%
-71.2631
0.2%
-71.2621
0.2%
-71.25751
0.2%
-71.2551
0.2%
-71.24751
0.2%
-71.2471
0.2%
ValueCountFrequency (%)
-70.811
0.2%
-70.832
0.4%
-70.8331
0.2%
-70.85251
0.2%
-70.8531
0.2%
-70.8551
0.2%
-70.861
0.2%
-70.88751
0.2%
-70.90751
0.2%
-70.9151
0.2%

LAT
Real number (ℝ)

Distinct376
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.21644
Minimum42.03
Maximum42.381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:57.837808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42.03
5-th percentile42.10745
Q142.180775
median42.2181
Q342.25225
95-th percentile42.31985
Maximum42.381
Range0.351
Interquartile range (IQR)0.071475

Descriptive statistics

Standard deviation0.061777184
Coefficient of variation (CV)0.0014633442
Kurtosis0.10400249
Mean42.21644
Median Absolute Deviation (MAD)0.03625
Skewness-0.086678598
Sum21361.519
Variance0.0038164205
MonotonicityNot monotonic
2025-10-01T10:53:57.961302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.235
 
1.0%
42.1924
 
0.8%
42.20754
 
0.8%
42.1884
 
0.8%
42.2454
 
0.8%
42.3163
 
0.6%
42.2553
 
0.6%
42.3173
 
0.6%
42.1783
 
0.6%
42.27653
 
0.6%
Other values (366)470
92.9%
ValueCountFrequency (%)
42.031
0.2%
42.04851
0.2%
42.0521
0.2%
42.0592
0.4%
42.06751
0.2%
42.07252
0.4%
42.07351
0.2%
42.07752
0.4%
42.07951
0.2%
42.08251
0.2%
ValueCountFrequency (%)
42.3811
0.2%
42.3741
0.2%
42.37152
0.4%
42.35251
0.2%
42.3462
0.4%
42.3452
0.4%
42.34251
0.2%
42.341
0.2%
42.3391
0.2%
42.33821
0.2%

MEDV
Real number (ℝ)

High correlation 

Distinct228
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.528854
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:58.089747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.1821759
Coefficient of variation (CV)0.40757404
Kurtosis1.5167834
Mean22.528854
Median Absolute Deviation (MAD)4
Skewness1.1109119
Sum11399.6
Variance84.312354
MonotonicityNot monotonic
2025-10-01T10:53:58.218259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
21.77
 
1.4%
23.17
 
1.4%
20.66
 
1.2%
226
 
1.2%
19.46
 
1.2%
23.95
 
1.0%
205
 
1.0%
22.25
 
1.0%
Other values (218)435
86.0%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.21
 
0.2%
8.32
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

CRIM
Real number (ℝ)

High correlation 

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6135236
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:58.351243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.6015451
Coefficient of variation (CV)2.3803761
Kurtosis37.130509
Mean3.6135236
Median Absolute Deviation (MAD)0.22145
Skewness5.2231488
Sum1828.4429
Variance73.986578
MonotonicityNot monotonic
2025-10-01T10:53:58.491439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.034661
 
0.2%
0.050831
 
0.2%
0.037381
 
0.2%
0.039611
 
0.2%
0.034271
 
0.2%
0.050231
 
0.2%
0.033061
 
0.2%
0.054971
 
0.2%
Other values (494)494
97.6%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

ZN
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:58.606810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.322453
Coefficient of variation (CV)2.0523759
Kurtosis4.0315101
Mean11.363636
Median Absolute Deviation (MAD)0
Skewness2.2256663
Sum5750
Variance543.93681
MonotonicityNot monotonic
2025-10-01T10:53:58.718449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0372
73.5%
2021
 
4.2%
8015
 
3.0%
2210
 
2.0%
2510
 
2.0%
12.510
 
2.0%
407
 
1.4%
306
 
1.2%
456
 
1.2%
905
 
1.0%
Other values (16)44
 
8.7%
ValueCountFrequency (%)
0372
73.5%
12.510
 
2.0%
17.51
 
0.2%
181
 
0.2%
2021
 
4.2%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
283
 
0.6%
306
 
1.2%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8015
3.0%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

INDUS
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.136779
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:58.857562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8603529
Coefficient of variation (CV)0.61600874
Kurtosis-1.2335396
Mean11.136779
Median Absolute Deviation (MAD)6.32
Skewness0.29502157
Sum5635.21
Variance47.064442
MonotonicityNot monotonic
2025-10-01T10:53:58.988489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1132
26.1%
19.5830
 
5.9%
8.1422
 
4.3%
6.218
 
3.6%
21.8915
 
3.0%
3.9712
 
2.4%
9.912
 
2.4%
10.5911
 
2.2%
8.5611
 
2.2%
5.8610
 
2.0%
Other values (66)233
46.0%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.657
 
1.4%
21.8915
 
3.0%
19.5830
 
5.9%
18.1132
26.1%
15.043
 
0.6%
13.925
 
1.0%
13.894
 
0.8%
12.836
 
1.2%
11.935
 
1.0%

CHAS
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Length

2025-10-01T10:53:59.103309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T10:53:59.177234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0471
93.1%
135
 
6.9%

NOX
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55469506
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:59.270901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11587768
Coefficient of variation (CV)0.20890339
Kurtosis-0.064667133
Mean0.55469506
Median Absolute Deviation (MAD)0.0875
Skewness0.72930792
Sum280.6757
Variance0.013427636
MonotonicityNot monotonic
2025-10-01T10:53:59.415949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53823
 
4.5%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.69314
 
2.8%
0.60514
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.0%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

RM
Real number (ℝ)

High correlation 

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2846344
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:59.548222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.70261714
Coefficient of variation (CV)0.11179921
Kurtosis1.8915004
Mean6.2846344
Median Absolute Deviation (MAD)0.3455
Skewness0.40361213
Sum3180.025
Variance0.49367085
MonotonicityNot monotonic
2025-10-01T10:53:59.677749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2293
 
0.6%
6.1273
 
0.6%
5.7133
 
0.6%
6.4173
 
0.6%
6.4053
 
0.6%
6.1673
 
0.6%
5.3042
 
0.4%
5.392
 
0.4%
6.1932
 
0.4%
4.1382
 
0.4%
Other values (436)480
94.9%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

AGE
Real number (ℝ)

High correlation 

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.574901
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:53:59.806924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.148861
Coefficient of variation (CV)0.41048344
Kurtosis-0.96771559
Mean68.574901
Median Absolute Deviation (MAD)19.55
Skewness-0.59896264
Sum34698.9
Variance792.3584
MonotonicityNot monotonic
2025-10-01T10:53:59.955396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10043
 
8.5%
87.94
 
0.8%
97.94
 
0.8%
98.84
 
0.8%
964
 
0.8%
98.24
 
0.8%
95.44
 
0.8%
96.23
 
0.6%
21.43
 
0.6%
883
 
0.6%
Other values (346)430
85.0%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
ValueCountFrequency (%)
10043
8.5%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.24
 
0.8%

DIS
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7950427
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:54:00.090411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.1057101
Coefficient of variation (CV)0.55485809
Kurtosis0.48794112
Mean3.7950427
Median Absolute Deviation (MAD)1.29115
Skewness1.0117806
Sum1920.2916
Variance4.4340151
MonotonicityNot monotonic
2025-10-01T10:54:00.294059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
6.81474
 
0.8%
5.28734
 
0.8%
5.40074
 
0.8%
5.72094
 
0.8%
6.4983
 
0.6%
6.47983
 
0.6%
7.3093
 
0.6%
3.94543
 
0.6%
6.33613
 
0.6%
Other values (402)470
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

RAD
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5494071
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:54:00.439413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7072594
Coefficient of variation (CV)0.91181152
Kurtosis-0.86723199
Mean9.5494071
Median Absolute Deviation (MAD)2
Skewness1.0048146
Sum4832
Variance75.816366
MonotonicityNot monotonic
2025-10-01T10:54:00.565303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.7%
4110
21.7%
338
 
7.5%
626
 
5.1%
824
 
4.7%
224
 
4.7%
120
 
4.0%
717
 
3.4%
ValueCountFrequency (%)
120
 
4.0%
224
 
4.7%
338
 
7.5%
4110
21.7%
5115
22.7%
626
 
5.1%
717
 
3.4%
824
 
4.7%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.7%
717
 
3.4%
626
 
5.1%
5115
22.7%
4110
21.7%
338
 
7.5%
224
 
4.7%
120
 
4.0%

TAX
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.23715
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:54:00.719861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.53712
Coefficient of variation (CV)0.4128412
Kurtosis-1.142408
Mean408.23715
Median Absolute Deviation (MAD)73
Skewness0.66995594
Sum206568
Variance28404.759
MonotonicityNot monotonic
2025-10-01T10:54:00.925676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
33010
 
2.0%
Other values (56)219
43.3%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

PTRATIO
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455534
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-01T10:54:01.108001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1649455
Coefficient of variation (CV)0.11730604
Kurtosis-0.28509138
Mean18.455534
Median Absolute Deviation (MAD)1.15
Skewness-0.80232493
Sum9338.5
Variance4.6869891
MonotonicityNot monotonic
2025-10-01T10:54:01.292759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.5%
19.219
 
3.8%
17.418
 
3.6%
19.117
 
3.4%
18.617
 
3.4%
18.416
 
3.2%
16.616
 
3.2%
Other values (36)179
35.4%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.33
 
0.6%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

Interactions

2025-10-01T10:53:54.632658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.049202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:23.752850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.909392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:29.417241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:32.522017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:35.776855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:39.809883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.658968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:46.342865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.766873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.737966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.000431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.299793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.721835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.238654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:24.032387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.067530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:29.558348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:32.681861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:36.315709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:39.980889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.837550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:46.705406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.895293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.828035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.088715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.399185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.169251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.389008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:24.579054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.225184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:29.704973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:32.833648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:36.553410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:40.263236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:43.015080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:46.998021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:49.415612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.921111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.182219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.489042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.268334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.490057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:24.794873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.420639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:30.217086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.003597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:36.955553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:40.550591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:43.170178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.175366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:49.552411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.011498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.286121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.601336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.358915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.577373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:25.006351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.584827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:30.405077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.196403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:37.328457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:40.909743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:43.416625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.419832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:49.705059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.096995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.374785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.688918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.453652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.693036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:25.215722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.736719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:30.553000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.340504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:37.617159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:41.079952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:43.596900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.600210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:49.834250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.187710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.464262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.784241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.539034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:20.919576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:25.526428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:27.925828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:30.697733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.514025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:37.786982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:41.279798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:43.887009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.737910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:49.972773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.276154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.576622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.877733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.633353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:21.291082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:25.783484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.100021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:30.867774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.679311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:37.965097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:41.528636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:44.106647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.872921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.097030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.364990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.667139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.976023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.736054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:21.549971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:25.924257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.283682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:31.113569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.818431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:38.105755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:41.679575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:44.310367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:47.997588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.181095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.456402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.760702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.062245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.832715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:21.960070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.063322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.471208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:31.329023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:33.967107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:38.286838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:41.868913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:44.521371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.124870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.265890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.553549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.849772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.162189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:55.915572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:22.358346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.229697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.621435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:31.600982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:34.146486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:38.463449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.005019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:45.221802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.257798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.361238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.642031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:52.943771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.254154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:56.000776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:22.772631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.421748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.767276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:31.878919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:34.490723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:38.728204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.150243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:45.733507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.389359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.471651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.734778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.030151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.341684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:56.091860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:23.179125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.577685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:28.962125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:32.146321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:35.047363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:39.352355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.320691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:45.893476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.511795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.558136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.823308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.120405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.437662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:56.178796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:23.488095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:26.749642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:29.229936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:32.308784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:35.428295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:39.608269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:42.492898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:46.068628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:48.641183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:50.648098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:51.913610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:53.212502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-01T10:53:54.524429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-01T10:54:01.451041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGECHASCRIMDISINDUSLATLONMEDVNOXPTRATIORADRMTAXTRACTZN
AGE1.0000.0000.704-0.8020.6790.0700.194-0.5520.7950.3550.418-0.2780.526-0.530-0.544
CHAS0.0001.0000.0000.0840.1460.3240.2750.2080.1750.1570.1310.0120.0430.3010.024
CRIM0.7040.0001.000-0.7450.736-0.1510.087-0.5630.8210.4650.728-0.3090.729-0.641-0.572
DIS-0.8020.084-0.7451.000-0.757-0.013-0.0610.446-0.880-0.322-0.4960.263-0.5740.5670.615
INDUS0.6790.1460.736-0.7571.000-0.0210.081-0.5800.7910.4340.456-0.4150.664-0.557-0.643
LAT0.0700.324-0.151-0.013-0.0211.0000.1730.021-0.081-0.005-0.306-0.119-0.164-0.340-0.105
LON0.1940.2750.087-0.0610.0810.1731.000-0.4210.2370.360-0.037-0.3030.050-0.279-0.221
MEDV-0.5520.208-0.5630.446-0.5800.021-0.4211.000-0.566-0.554-0.3510.635-0.5670.5190.439
NOX0.7950.1750.821-0.8800.791-0.0810.237-0.5661.0000.3910.586-0.3100.650-0.600-0.635
PTRATIO0.3550.1570.465-0.3220.434-0.0050.360-0.5540.3911.0000.318-0.3130.453-0.572-0.448
RAD0.4180.1310.728-0.4960.456-0.306-0.037-0.3510.5860.3181.000-0.1070.705-0.532-0.279
RM-0.2780.012-0.3090.263-0.415-0.119-0.3030.635-0.310-0.313-0.1071.000-0.2720.2900.361
TAX0.5260.0430.729-0.5740.664-0.1640.050-0.5670.6500.4530.705-0.2721.000-0.637-0.371
TRACT-0.5300.301-0.6410.567-0.557-0.340-0.2790.519-0.600-0.572-0.5320.290-0.6371.0000.446
ZN-0.5440.024-0.5720.615-0.643-0.105-0.2210.439-0.635-0.448-0.2790.361-0.3710.4461.000

Missing values

2025-10-01T10:53:56.330645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-01T10:53:56.469298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TOWNTRACTLONLATMEDVCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIO
0Nahant2011-70.955042.255024.00.0063218.02.3100.5386.57565.24.0900129615.3
1Swampscott2021-70.950042.287521.60.027310.07.0700.4696.42178.94.9671224217.8
2Swampscott2022-70.936042.283034.70.027290.07.0700.4697.18561.14.9671224217.8
3Marblehead2031-70.928042.293033.40.032370.02.1800.4586.99845.86.0622322218.7
4Marblehead2032-70.922042.298036.20.069050.02.1800.4587.14754.26.0622322218.7
5Marblehead2033-70.916542.304028.70.029850.02.1800.4586.43058.76.0622322218.7
6Salem2041-70.936042.297022.90.0882912.57.8700.5246.01266.65.5605531115.2
7Salem2042-70.937542.310022.10.1445512.57.8700.5246.17296.15.9505531115.2
8Salem2043-70.933042.312016.50.2112412.57.8700.5245.631100.06.0821531115.2
9Salem2044-70.929042.316018.90.1700412.57.8700.5246.00485.96.5921531115.2
TOWNTRACTLONLATMEDVCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIO
496Revere1704-71.001042.252519.70.289600.09.6900.5855.39072.92.7986639119.2
497Revere1705-70.994742.249618.30.268380.09.6900.5855.79470.62.8927639119.2
498Revere1706-71.005042.245521.20.239120.09.6900.5856.01965.32.4091639119.2
499Revere1707-70.998542.243017.50.177830.09.6900.5855.56973.52.3999639119.2
500Revere1708-70.992042.238016.80.224380.09.6900.5856.02779.72.4982639119.2
501Winthrop1801-70.986042.231222.40.062630.011.9300.5736.59369.12.4786127321.0
502Winthrop1802-70.991042.227520.60.045270.011.9300.5736.12076.72.2875127321.0
503Winthrop1803-70.994842.226023.90.060760.011.9300.5736.97691.02.1675127321.0
504Winthrop1804-70.987542.224022.00.109590.011.9300.5736.79489.32.3889127321.0
505Winthrop1805-70.982542.221019.00.047410.011.9300.5736.03080.82.5050127321.0